Part I - Ford GoBike Data:

Demographics of Bikers Who Use Ford Bikes

by Sofiyah Olaiwon

Introduction

This document explores a dataset which includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area during the month of February.

Preliminary Wrangling

# Import all necessary libraries
import pandas as pd
import numpy as np
import datetime

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

%matplotlib inline

# Load dataset into a pandas dataframe and read first 5 rows
bike_data = pd.read_csv('201902-fordgobike-tripdata.csv')
bike_data.tail()

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
183407	480	2019-02-01 00:04:49.7240	2019-02-01 00:12:50.0340	27.0	Beale St at Harrison St	37.788059	-122.391865	324.0	Union Square (Powell St at Post St)	37.788300	-122.408531	4832	Subscriber	1996.0	Male	No
183408	313	2019-02-01 00:05:34.7440	2019-02-01 00:10:48.5020	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	66.0	3rd St at Townsend St	37.778742	-122.392741	4960	Subscriber	1984.0	Male	No
183409	141	2019-02-01 00:06:05.5490	2019-02-01 00:08:27.2200	278.0	The Alameda at Bush St	37.331932	-121.904888	277.0	Morrison Ave at Julian St	37.333658	-121.908586	3824	Subscriber	1990.0	Male	Yes
183410	139	2019-02-01 00:05:34.3600	2019-02-01 00:07:54.2870	220.0	San Pablo Ave at MLK Jr Way	37.811351	-122.273422	216.0	San Pablo Ave at 27th St	37.817827	-122.275698	5095	Subscriber	1988.0	Male	No
183411	271	2019-02-01 00:00:20.6360	2019-02-01 00:04:52.0580	24.0	Spear St at Folsom St	37.789677	-122.390428	37.0	2nd St at Folsom St	37.785000	-122.395936	1057	Subscriber	1989.0	Male	No

# Checking for incorrect datatypes and missing values
bike_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB

What is the structure of the dataset?

The dataset contains data of approxiamtely 180,000 rows and 16 columns documenting instances of bikes rented by ford over a period of months. The data contains information in columns encoded in numeric, object and categorical data types.

What is the main feature of interest in the dataset?

I am most interested in the various characteristics of people who use bikes from the Ford GoBike System.

What features in the dataset will help support investigation into the features of interest?

I will be making use of the duration of time spent on the bikes in seconds, the longitude and latitude of both the the start and end stations, the user type, member birth year, member gender and bike share for all trips features to conduct my analysis.

Data Cleaning

Checking for data issues and cleaning accordingly.

# Make a copy of the original dataset 
bike_copy = bike_data.copy()

# Checking for missing data 
bike_copy.isna().sum()

duration_sec                  0
start_time                    0
end_time                      0
start_station_id            197
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id              197
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64

# Drop rows that contain missing data
bike_copy.dropna(inplace=True)

# Checking for duplicates in data
bike_copy.duplicated().sum()

0

bike_copy.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             174952 non-null  int64  
 1   start_time               174952 non-null  object 
 2   end_time                 174952 non-null  object 
 3   start_station_id         174952 non-null  float64
 4   start_station_name       174952 non-null  object 
 5   start_station_latitude   174952 non-null  float64
 6   start_station_longitude  174952 non-null  float64
 7   end_station_id           174952 non-null  float64
 8   end_station_name         174952 non-null  object 
 9   end_station_latitude     174952 non-null  float64
 10  end_station_longitude    174952 non-null  float64
 11  bike_id                  174952 non-null  int64  
 12  user_type                174952 non-null  object 
 13  member_birth_year        174952 non-null  float64
 14  member_gender            174952 non-null  object 
 15  bike_share_for_all_trip  174952 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.7+ MB

Data Issues

• Most of the columns in the dataframe seem to have incorrect data types.
• user_type, member_gender, bike_share_for_all_trip columns are all categorical data.
• start_time and end_time columns should be in datetime datatype
• start_station_id, end_station_id, bike_id should all be object data types since they are unique.
• Calculating ages of bike riders using the members_birth_year column

# Checking for the unique values in the gender columns
bike_copy.member_gender.value_counts()

Male      130500
Female     40805
Other       3647
Name: member_gender, dtype: int64

# Checking for the unique values in the user_type column
bike_copy.user_type.value_counts()

Subscriber    158386
Customer       16566
Name: user_type, dtype: int64

# Checking for the unique values in the bike_share_for_all_trip
bike_copy.bike_share_for_all_trip.value_counts()

No     157606
Yes     17346
Name: bike_share_for_all_trip, dtype: int64

# convert cut, color, and clarity into ordered categorical types
ordinal_var_dict = {'member_gender': ['Male', 'Female', 'Other'],
                    'user_type': ['Subscriber', 'Customer'],
                    'bike_share_for_all_trip': ['No', 'Yes']}

for var in ordinal_var_dict:
    
        ordered_var = pd.api.types.CategoricalDtype(ordered = True,
                                                    categories = ordinal_var_dict[var])
        bike_copy[var] = bike_copy[var].astype(ordered_var)   

# Converting start_time and end_time columns to datetime columns
bike_copy['start_time'] = pd.to_datetime(bike_copy['start_time'])
bike_copy['end_time'] = pd.to_datetime(bike_copy['end_time'])

# Changing the data type of the below columns fron float to integers
id_col = ['start_station_id', 'end_station_id', 'bike_id', 'member_birth_year']
bike_copy[id_col] = bike_copy[id_col].astype('int64')

# Calculating ages of bike riders using the members_birth_year column 
bike_copy['member_age'] = (datetime.datetime.now().year - bike_copy['member_birth_year']).astype('int64')

bike_copy.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 17 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  int64         
 4   start_station_name       174952 non-null  object        
 5   start_station_latitude   174952 non-null  float64       
 6   start_station_longitude  174952 non-null  float64       
 7   end_station_id           174952 non-null  int64         
 8   end_station_name         174952 non-null  object        
 9   end_station_latitude     174952 non-null  float64       
 10  end_station_longitude    174952 non-null  float64       
 11  bike_id                  174952 non-null  int64         
 12  user_type                174952 non-null  category      
 13  member_birth_year        174952 non-null  int64         
 14  member_gender            174952 non-null  category      
 15  bike_share_for_all_trip  174952 non-null  category      
 16  member_age               174952 non-null  int64         
dtypes: category(3), datetime64[ns](2), float64(4), int64(6), object(2)
memory usage: 20.5+ MB

# Checking the summaryt statistics of the dataset
bike_copy.describe()

	duration_sec	start_station_id	start_station_latitude	start_station_longitude	end_station_id	end_station_latitude	end_station_longitude	bike_id	member_birth_year	member_age
count	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000
mean	704.002744	139.002126	37.771220	-122.351760	136.604486	37.771414	-122.351335	4482.587555	1984.803135	37.196865
std	1642.204905	111.648819	0.100391	0.117732	111.335635	0.100295	0.117294	1659.195937	10.118731	10.118731
min	61.000000	3.000000	37.317298	-122.453704	3.000000	37.317298	-122.453704	11.000000	1878.000000	21.000000
25%	323.000000	47.000000	37.770407	-122.411901	44.000000	37.770407	-122.411647	3799.000000	1980.000000	30.000000
50%	510.000000	104.000000	37.780760	-122.398279	101.000000	37.781010	-122.397437	4960.000000	1987.000000	35.000000
75%	789.000000	239.000000	37.797320	-122.283093	238.000000	37.797673	-122.286533	5505.000000	1992.000000	42.000000
max	84548.000000	398.000000	37.880222	-121.874119	398.000000	37.880222	-121.874119	6645.000000	2001.000000	144.000000

Now the data has been cleaned, lets start exploring.

Univariate Exploration

Question

What is the distribution of riders based on gender?

Visualization

# Using seaborns countplot to see the distribution of bikers based on gender
base_color = sns.color_palette()[0]
sns.countplot(x='member_gender', data=bike_copy, color=base_color)
plt.title('Distribution of Members Based On Gender');

# Using pandas plot function 
(bike_copy.member_gender.value_counts(normalize=True)*100).plot(kind='bar')
plt.xticks(rotation=0)
plt.xlabel('Gender')
plt.ylabel('Percentage')
plt.title('Percentage of Members Based On Gender');

(bike_copy.member_gender.value_counts(normalize=True)*100)

Male      74.591888
Female    23.323540
Other      2.084572
Name: member_gender, dtype: float64

Observation

A large percentage of bikers are male who constitute to about 75% of the member population and females with about 23% and other genders with about 2%.

Question

Which User Type Ride Bikes More?

Visualization

# Using seaborn countplot function to view the distribution of user types
sns.countplot(x='user_type', data=bike_copy, color=base_color)
plt.title('Distribution of Type Of Bike Users');

Observation

Subscribers make use of the bikes than regular customers

Question

How likely are bikers to share a gobike during a trip?

Visualization

# Using seaborn countplot function to view the distribution of bile sharing
sns.countplot(x='bike_share_for_all_trip', data=bike_copy, color=base_color);

Observation

Most bike users prefer Not to share bikes during a trip.

# The duration_sec and member_age have houge outliers that could affect our analysis
# Subsetting for rides below 4000 seconds and member ages below 70
bike_copy = bike_copy[(bike_copy['duration_sec'] <= 4000) & (bike_copy['member_age'] <= 70)].reset_index(drop=True)
bike_copy.describe()
bike_copy.head()

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip	member_age
0	1585	2019-02-28 23:54:18.549	2019-03-01 00:20:44.074	7	Frank H Ogawa Plaza	37.804562	-122.271738	222	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974	Male	Yes	48
1	1793	2019-02-28 23:49:58.632	2019-03-01 00:19:51.760	93	4th St at Mission Bay Blvd S	37.770407	-122.391198	323	Broadway at Kearny	37.798014	-122.405950	5200	Subscriber	1959	Male	No	63
2	1147	2019-02-28 23:55:35.104	2019-03-01 00:14:42.588	300	Palm St at Willow St	37.317298	-121.884995	312	San Jose Diridon Station	37.329732	-121.901782	3803	Subscriber	1983	Female	No	39
3	1615	2019-02-28 23:41:06.766	2019-03-01 00:08:02.756	10	Washington St at Kearny St	37.795393	-122.404770	127	Valencia St at 21st St	37.756708	-122.421025	6329	Subscriber	1989	Male	No	33
4	1570	2019-02-28 23:41:48.790	2019-03-01 00:07:59.715	10	Washington St at Kearny St	37.795393	-122.404770	127	Valencia St at 21st St	37.756708	-122.421025	6548	Subscriber	1988	Other	No	34

Question

What is the age range of ford gobikes members?

Visualization

bin_edges = np.arange(20, bike_copy['member_age'].max()+5, 5)
plt.hist(data=bike_copy, x='member_age', bins=bin_edges)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Distribution Of Ages Of Bike Users');

# Using a boxplot to views the statistical distribution and check for outliers
sns.boxplot(x='member_age', data=bike_copy);

# Looking at distribution of ages between 20 and 70
bin_edges = np.arange(20, bike_copy['member_age'].max()+3, 3)
plt.hist(data=bike_copy, x='member_age', bins=bin_edges)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Distribution Of Ages Of Bike Users');

Observation

While we have riders with ages ranging from 20 to bout 80, a large percentage of bikers fall into the age range of 25 - 45.

Question

What period of time do most bikers spend cycling?

Visualization

# Viewing the distribution of duration in seconds 
sns.displot(x='duration_sec', data=bike_copy);

# Using a histogram with a log scale on x axis to visualize 
ticks = [3, 10, 30, 100, 300, 1000, 3000]
labels = ['{}'.format(x) for x in ticks]
plt.figure(figsize=[8, 5])
bin_edges = np.arange(0, bike_copy.duration_sec.max()+20, 20)
plt.hist(x='duration_sec', data=bike_copy, bins=bin_edges)
plt.xscale('log')
plt.xlabel('Duration (Seconds)')
plt.ylabel('Frequency')
plt.xticks(ticks, labels)
plt.xlim(30, 5000);

Observation

Although most riders prefer to take short trips on the bikes, some bikers spend hours\ on bikes. This will be investigated in futher EDA analysis.

Question

Are some bike stations more frequented than others?

Visualization

# Adjust bin edges to show true distribution of  start station id
bin_edges = np.arange(0, bike_copy.start_station_id.max()+5, 5)
plt.figure(figsize=[8, 5])
plt.hist(x='start_station_id', data=bike_copy, bins=bin_edges)
plt.xlabel('station_id')
plt.ylabel('Frequency')
plt.title('Distribution of start station id');

# Same for end station. Seems like both plots look alike 
plt.figure(figsize=[8, 5])
plt.hist(x='end_station_id', data=bike_copy, bins=bin_edges)
plt.xlabel('station_id')
plt.ylabel('Frequency')
plt.title('Distribution of end station id');

Observation

The distribution plots of both start and end station ids look similar meaning some stations are more frequented than others.

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?

From the analysis done above, there is a gender imbalance in the data having about 75% male riders.
A large percentage of bike riders are subscribers and most riders prefer not to share rides during a trip

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

The member age and duration had huge outlier values that could affect our analysis which were eventually dropped during the course of analysis. The histogram plot of the start and end station id were similar in nature which means that some bike stations are more frequented or accessible than others. I also performed a log transformation on the x axis of the histogram plot of duration sec which shows a normal distribution of bike ride durations overall.

Bivariate Exploration

In this section, investigate relationships between pairs of variables in your data. Make sure the variables that you cover here have been introduced in some fashion in the previous section (univariate exploration).

Question

What is the distribution of the duration of time spent on ford gobikes across the month of february?

Visualization

# Using pandas .plot metgod plot a timeseries of the start_time vs duration sec
fig, ax = plt.subplots(figsize=(15, 6));
bike_copy.plot(x='start_time', y='duration_sec', ax=ax, legend=False)
plt.xlabel('Start Time')
plt.ylabel('Duration (seconds)')
plt.title('Time Plot Series of Duration Spent On Bikes');

Observation

The timeseries plot above shows a periodic distribution with high spikes showing that ta specific time of the days bikers take very long rides on the bikes and also at specific times there are little ore no use of bikes at that periods.

Question

Which categories of customers takes the longest rides.

Visualization

# Lets plot all three categorical groups together
fig, ax = plt.subplots(nrows = 3, figsize=[8, 12])
sns.boxplot(x='member_gender', y='duration_sec', data=bike_copy, color=base_color, ax=ax[0])
sns.boxplot(x='user_type', y='duration_sec', data=bike_copy, color=base_color, ax=ax[1])
sns.boxplot(x='bike_share_for_all_trip', y='duration_sec', data=bike_copy, color=base_color, ax=ax[2]);

Observation

From the plot above it shows that on an average females and other genders take slighly longer rides than females although percentage of males who ride bikes are higher than others. Same with subscribers and customers, whereby customers have an average duration on bikes than subscribers who constititute more in the data. This could be due to the imbalance of the class/categorical data.

bike_copy.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 172728 entries, 0 to 172727
Data columns (total 17 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             172728 non-null  int64         
 1   start_time               172728 non-null  datetime64[ns]
 2   end_time                 172728 non-null  datetime64[ns]
 3   start_station_id         172728 non-null  int64         
 4   start_station_name       172728 non-null  object        
 5   start_station_latitude   172728 non-null  float64       
 6   start_station_longitude  172728 non-null  float64       
 7   end_station_id           172728 non-null  int64         
 8   end_station_name         172728 non-null  object        
 9   end_station_latitude     172728 non-null  float64       
 10  end_station_longitude    172728 non-null  float64       
 11  bike_id                  172728 non-null  int64         
 12  user_type                172728 non-null  category      
 13  member_birth_year        172728 non-null  int64         
 14  member_gender            172728 non-null  category      
 15  bike_share_for_all_trip  172728 non-null  category      
 16  member_age               172728 non-null  int64         
dtypes: category(3), datetime64[ns](2), float64(4), int64(6), object(2)
memory usage: 18.9+ MB

Lets look at a PairGrid and heatmap of numerical values in the data set to look for correlation between variables!

# Listing out all numerical values
numerical_col = ['duration_sec', 'start_station_id', 'end_station_id', 'bike_id', 'member_birth_year', 'member_age']

correlation = bike_copy[numerical_col].corr()
sns.heatmap(correlation, cmap = 'vlag_r');

#  plot matrix: sample 1000 entries
samples = np.random.choice(bike_copy.shape[0], 1000, replace = False)
bike_sample = bike_copy.loc[samples,:]

g = sns.PairGrid(data = bike_sample, vars = ['duration_sec', 'start_station_id', 'end_station_id', 'bike_id', 'member_birth_year', 'member_age'])
g = g.map_diag(plt.hist, bins = 20)                 
g.map_offdiag(plt.scatter, alpha = 0.1);

Seems that there is little or no correlation between most of the columns with each other apart from member birth year and member age which makes a lot of sense. There is also correlation between start station id and end station id

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

From the boxplot plot above it shows that on an average females and other genders take slighly longer rides than females although percentage of males who ride bikes are higher than others. Same with subscribers and customers, whereby customers have an average duration on bikes than subscribers who constititute more in the population. This could be due to the imbalance of the class/categorical data.

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

The timeplot plot series plotted above, there is periodic distribution of of duration at time periods showing that at specific time of the days bikers take very long rides on the bikes and also at specific times there are little ore no use of bikes at that periods.

Multivariate Exploration

Create plots of three or more variables to investigate your data even further. Make sure that your investigations are justified, and follow from your work in the previous sections.

Question

What is the average time spent on bikes by each gender type and does it vary based on whether they are subscribers or customers?

Visualization

# creating a function to plot multivariate plots
def multiplot(data, x, hue):
    """Plots a multivariate plot based on the duration of time"""
    sns.barplot(data=data, x=x, y='duration_sec', hue=hue)
    plt.xlabel('{}'.format(x))
    plt.ylabel('Duration (sec)');
    
multiplot(bike_copy, 'member_gender', 'user_type')

Observation

Based on the user type, subscribers spend lesser time on bikes than customers. For subscribers, other gender spend the most time on bikes followed by females and then males. For customers, females spend the most time on bikes.

Question

What is the average time spent on bikes by each user type and does it vary based on whether they share bikes for all trips?

Visualization

# Using the multi[plot function created earlier
multiplot(bike_copy, 'user_type', 'bike_share_for_all_trip')

Observation

Interstingly, no customers shared their bikes while on a trip. This may be due to the fact that maybe the bike sharing feature not being available to non-subscribers but since may be just a speculation as we dont have any information on this.

Question

Lets make use of our start station latitude, longitudes and ids, end station latitude, longitudes and ids to create a scatter map box that will show is the various areas that bikers start and end their rides.

Visualization

# using plotly library to create a mapbox
fig = px.scatter_mapbox(
    bike_copy,  # Our DataFrame
    lat='start_station_latitude',
    lon='start_station_longitude',
    width=800,  # Width of map
    height=600,  # Height of map
    color='start_station_id',
    hover_data=["start_station_id"],  # Display duration(sec) when hovering mouse
)

fig.update_layout(mapbox_style="open-street-map")

fig.show()

# using plotly library to create a mapbox
fig = px.scatter_mapbox(
    bike_copy,  # Our DataFrame
    lat='end_station_latitude',
    lon='end_station_longitude',
    width=800,  # Width of map
    height=600,  # Height of map
    color='end_station_id',
    hover_data=["end_station_id"],  # Display duration when hovering mouse
)

fig.update_layout(mapbox_style="open-street-map")

fig.show()

Observation

Based on the color distribution of station id on the scatter map box, there is a color pattern that shows that there is a relationship between location and station id or to better put it certain location are designated to a range of station ids.

# saving cleaned dataframe to bike_cleaned.csv so it can be used for explanatory phase
bike_copy.to_csv('bike_cleaned.csv', index=False)

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

• I plotted a scatter_mapbox to investigate the bike stations and there I discovered the major areas in San Fransisco where they are located which are San Jose, San Franscisco, Oakland and Berkerley.
• Based on the user type, subscribers spend lesser time on bikes than customers. For subscribers, other gender spend the most time on bikes followed by females and then males. For customers, females spend the most time on bikes.

Were there any interesting or surprising interactions between features?

Based on the color distribution of station id on the scatter map box, there is a color pattern that shows that there is a relationship between location and station id or to better put it certain location are designated to a range of station ids.
Interstingly, no customers shared their bikes while on a trip. This may be due to the fact that maybe the bike sharing feature not being available to non-subscribers but since may be just a speculation as we dont have any information on this.

Conclusions

From this analysis, we can observe the following about the GoBike users.

• Most riders prefer to take short rides between 2 to 45 minutes while others spend hours.
• 75% of GoBike members are males, 23% are females and 2% belongs to other genders.
• A large percentage of GoBike members are subscribers while others are customers and although subscribers constitute more to the data, customers take longer rides on an average than subscribers.
• The distribution plots of both start and end station ids look similar meaning some stations are more frequented than others.
• The major areas in San Fransisco where the bike stations are located are San Jose, San Franscisco, Oakland and Berkerley.
• While we have riders with ages ranging from 20 to about 80, a large percentage of bikers fall into the age range of 25 - 45.
• Interstingly, no customers shared their bikes while on a trip. This may be due to the fact that maybe the bike sharing feature not being available to non-subscribers but since may be just a speculation as we dont have any information on this.